Recommender Systems with Social Regularization

Hao Ma, Dengyong Zhou, Chao LiuMicrosoft Research

Michael R. LyuThe Chinese University of Hong Kong

Irwin KingAT&T Labs – Research

The Chinese University of Hong Kong

Recommender Systems are Everywhere

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Web 2.0 Web Sites are Everywhere

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Trust-aware Recommender Systems

• These Methods utilize the inferred implicit or observed explicit trust information to further improve traditional recommender systems.– [J. O’Donovan and B. Smyth, IUI 2005]– [P. Massa and P. Avesani, RecSys 2007]– [H. Ma, I. King, and M. R. Lyu, SIGIR 2009]

• Based on the motivation that “I trust you => I have similar tastes with you”.

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Comparison

• Trust-aware

– Trust network: unilateral relations

– Trust relations can be treated as “similar” relations

– Few datasets available on the Web

• Social-based

– Social friend network: mutual relations

– Friends are very diverse, and may have different tastes

– Lots of Web sites have social network implementation

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Contents of This Work

• Focusing on social-based recommendation problems

• Two methods are proposed based on matrix factorization with social regularization terms– Can be applied to trust-aware recommender systems.

• Experiments on two large datasets– Douban (social friend network)– Epinions (trust network)

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Problem Definition

Social Network Information User-Item Rating Matrix7

Low-Rank Matrix Factorization for Collaborative Filtering

• Objective function

Ui ,Vj : low dimension column vectors to represent user/item preferences.

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Social Regularization I

• Average-based regularization

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Minimize Ui’s taste with the average tastes of Ui’s friends. The similarity function Sim(i, f) allows the social regularization term to treat users’ friends differently.

Social Regularization I

• Gradients

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Social Regularization II

• Individual-based regularization

This approach allows similarity of friends’ tastes to be individually considered. It also indirectly models the propagation of tastes.

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Social Regularization II

• Gradients

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Similarity Function

• Vector Space Similarity (VSS) or Cosine Similarity

• Pearson Correlation Coefficient (PCC)

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Dataset I

• Douban– Chinese Web 2.0 Web site with social friend

network service– The largest online book, movie and music review

and rating site in China– We crawled 129,940 users and 58,541 movies with

16,830,839 movie ratings– The total number of friend links between users is

1,692,952

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Dataset II

• Epinions– A well-known English general consumer review

and rating site– Every member maintains a trust list which

presents a user network of trust relationships– We crawled 51,670 users who have rated a total of

83,509 different items. The total number of ratings is 631,064

– The total number of issued trust statements is 511,799

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Metrics

• MAE and RMSE

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Performance Comparison

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Impact of Parameter beta

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Impact of Similarity Functions

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Conclusions

• We proposed two new social recommendation methods

• Our approaches perform better than other traditional and trust-aware recommendation methods

• The methods scale well since the employed algorithm is linear with the observation of ratings

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Future Work

• Employ more accurate similarity functions

• Consider item side regularization

• Apply similar techniques to other social applications, like social search problems

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Thanks!

Q&A